Proceedings
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| Filter results4 paper(s) found. |
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1. Variability Analysis of Temperature and Humidity for Control Optimization of a Hybrid Dehumidifier with a Heating Module for GreenhousesProtected horticulture using greenhouses and also recently plant factories is becoming more popular, especially for high-value crops such as paprika, tomato, strawberry, due to year-round production of high yield and better quality crops under controlled environment. Temperature and humidity are most important ambient environmental factors for not only optimum crop growth but also disease control. This study was conducted to analyze vertical and spatial variability of temperature and humidity... Y. Seo, W. Lee, Y. Kim, S. Chung, S. Jang, I. Bae |
2. Effect of Irrigation Scheduling Technique and Fertility Level on Corn Yield and Nitrogen MovementFlorida has more first magnitude springs that anywhere in the world. Most of these are located in north Florida where agricultural production is the primary basis for the economy. Irrigated corn has become a popular part of the crop rotation in recent years. This project is a study of a corn and peanut rotation investigating Best Management Practices (BMPs) of nitrogen fertility level (336, 246, 157 kg/ha) and irrigation strategies as follows: (i) GROW, mimicking grower’s practices,... M. Dukes, M. Zamora, D. Rowland |
3. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
4. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |